Webp image compression system and compression method thereof
Through the various encoding methods and fidelity criteria of the Webp image compression system, the problem that the existing technology cannot take into account both image compression efficiency and quality is solved, and the flexibility and efficient compression effect are achieved according to the needs.
Patent Information
- Application Number
- CN202310685164.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-06-09
- Publication Date
- 2024-12-10
AI Technical Summary
The prior art cannot take into account both image compression efficiency and quality, and usually requires choosing a compression method, and cannot flexibly choose according to personal needs.
Provide a Webp image compression system, which adopts pixel encoding, predictive encoding and transform encoding, combines encoding redundancy, inter-pixel redundancy and reduction of psychological visual redundancy, and uses fidelity through encoder, decoder and signal transmission system. The criteria limit image distortion and enable the choice of multiple compression methods.
It realizes the selection of compression methods based on personal needs, taking into account the quality and efficiency of image compression, and can perform lossless or lossy compression when needed, meeting the needs of different applications.
Smart Images
Figure CN119110065A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of image compression, and in particular to a WebP image compression system and a compression method thereof. Background Art
[0002] Image compression refers to the technology of representing the original pixel matrix losslessly or with fewer bits, also known as image coding. The reason why image data can be compressed is because there is redundancy in the data. The redundancy of image data is manifested as: spatial redundancy caused by the correlation between adjacent pixels in the image; temporal redundancy caused by the correlation between different frames in the image sequence; spectral redundancy caused by the correlation between different color planes or spectral bands. The purpose of data compression is to reduce the number of bits required to represent the data by removing these data redundancies. Due to the huge amount of image data, it is very difficult to store, transmit and process it, so the compression of image data is very important.
[0003] Compression is divided into lossy compression and lossless compression. Generally speaking, lossy compression is more efficient than lossless compression, but there is a certain amount of information loss in the compression process of lossy compression, and the compressed data cannot completely restore the original image. Among traditional compression methods, only one method can be selected for compression, and it is impossible to take into account both image quality and compression efficiency. Summary of the invention
[0004] 1. Technical issues to be solved
[0005] In view of the shortcomings of the prior art, the present invention provides a Webp image compression system and a compression method thereof, which has the advantages of selecting different compression methods according to personal needs, taking into account both efficiency and image quality, and solves the problems raised in the above background technology.
[0006] (II) Technical solution
[0007] The present invention provides the following technical solutions: a Webp image compression system and a compression method thereof, wherein the system includes an image compression coding system, and the image compression coding system includes image coding and image decoding;
[0008] The image compression system compresses as follows:
[0009] Pixel coding: Each pixel is processed individually during coding, such as pulse code modulation, entropy coding and run length coding;
[0010] Predictive coding: By removing the correlation and redundancy between adjacent pixels, only new information is encoded. Commonly used methods include differential pulse code modulation;
[0011] Transform coding: Apply a certain transformation to a given image so that a large amount of information can be represented by less data. Commonly used transformations include: discrete Fourier transform, discrete residual transform and discrete wavelet transform.
[0012] Preferably, data redundancy includes three types: coding redundancy, inter-pixel redundancy and psychological visual redundancy. When one or more of them are reduced or eliminated, image compression is achieved.
[0013] Due to the correlation between pixels, any given pixel value can be predicted by its adjacent pixel values in principle, which leads to redundancy between pixels.
[0014] People observe images based on target features rather than pixels, which makes some information appear unimportant and can be ignored. The data representing this ignorable information is called psychological visual redundancy.
[0015] Preferably, the system includes an information transmission system, which includes an encoder, a decoder and signal transmission.
[0016] Preferably, the system includes a codec model, which includes a transformer and a quantizer. The transformer converts the input data, changes the description form of the data, and reduces or eliminates redundancy between pixels; the quantizer reduces the accuracy of the transformer output according to a given fidelity criterion to further reduce psychovisual redundancy.
[0017] Preferably, a fidelity criterion is used during the image encoding to evaluate the degree of distortion or quality of the image so as to limit the image distortion within a given range.
[0018] Fidelity criteria include objective fidelity criteria and subjective fidelity criteria; expressing the amount of information loss as a function of the original image and the reconstructed image obtained after compression and decompression is called the objective fidelity criterion, which is usually expressed in three forms: root mean square error, root mean square signal-to-noise ratio, and peak signal-to-noise ratio.
[0019] For visual images that are ultimately used as human visual experience, subjective evaluation is generally carried out using the subjective fidelity criteria, including comprehensive evaluation method and paired comparison scoring method.
[0020] Preferably, image encoding: compressing and encoding an image before storage, processing and transmission, also known as image compression;
[0021] Image decoding: Decompressing a compressed image to reconstruct the original image or its approximation.
[0022] Compared with the prior art, the present invention provides a WebP image compression system and a compression method thereof, which have the following beneficial effects:
[0023] The Webp image compression system and compression method thereof include multiple compression methods, such as pixel coding, prediction coding and transform coding. Users can use different methods to decompress images according to personal needs. If the compression efficiency is to be improved, lossy compression is used. If the image quality is to be guaranteed, lossless compression can be used. The system and method can well balance the image compression quality and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Figure 1 Schematic diagram of the information transmission system of the present invention. DETAILED DESCRIPTION
[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0026] See also Figure 1 , a WebP image compression system and a compression method thereof, the system includes an image compression coding system, the image compression coding system includes image coding and image decoding;
[0027] The image compression system compresses as follows:
[0028] Pixel coding: Each pixel is processed individually during coding, such as pulse code modulation, entropy coding and run length coding;
[0029] Predictive coding: By removing the correlation and redundancy between adjacent pixels, only new information is encoded. Commonly used methods include differential pulse code modulation;
[0030] Transform coding: applying a certain transformation to a given image so that a large amount of information can be represented by less data; commonly used transforms include: discrete Fourier transform, discrete residual transform and discrete wavelet transform.
[0031] There are three types of data redundancy: coding redundancy, inter-pixel redundancy and psychological visual redundancy. When one or more of them is reduced or eliminated, image compression is achieved.
[0032] Due to the correlation between pixels, any given pixel value can be predicted by its adjacent pixel values in principle, which leads to redundancy between pixels.
[0033] Lossless compression does not lose information after compression, and the original image can be restored without distortion after decoding the compressed image code; in practical applications, people do not require completely distortion-free information, but usually only require an approximate reproduction of the original information, that is, a certain amount of distortion is allowed. This compression code that limits the distortion within a certain allowable limit and can achieve a higher compression ratio is called limited distortion coding, also called lossy compression or coding. Through the method in this application, different methods can be selected for compression according to the user's personal needs, so that the image effect that the individual wants can be achieved, and thus both image quality and compression efficiency can be taken into account.
[0034] People observe images based on target features rather than pixels, which makes some information appear unimportant and can be ignored. The data representing this ignorable information is called psychological visual redundancy.
[0035] The system includes an information transmission system, which includes an encoder, a decoder and signal transmission. The system includes a coding and decoding model, which includes a transformer and a quantizer. The transformer converts the input data, changes the description form of the data, and reduces or eliminates the redundancy between pixels; the quantizer reduces the accuracy of the transformer output according to a given fidelity criterion to further reduce psychological visual redundancy. Image coding: compressing and encoding images before storage, processing and transmission, also known as image compression;
[0036] Image decoding: Decompressing a compressed image to reconstruct the original image or its approximation.
[0037] The fidelity criterion is used during the image encoding to evaluate the degree of distortion or quality of the image so as to limit the image distortion within a given range.
[0038] Fidelity criteria include objective fidelity criteria and subjective fidelity criteria; expressing the amount of information loss as a function of the original image and the reconstructed image obtained after compression and decompression is called the objective fidelity criterion, which is usually expressed in three forms: root mean square error, root mean square signal-to-noise ratio, and peak signal-to-noise ratio.
[0039] For visual images that are ultimately used as human visual experience, subjective evaluation is generally carried out using the subjective fidelity criteria, including comprehensive evaluation method and paired comparison scoring method.
[0040] An image is a source of information. The data describing the source of information is the sum of the amount of information (information entropy) and the amount of information redundancy. Image data compression technology is a method of studying how to use the redundancy of image data to reduce the amount of image data. Common information redundancy includes: time redundancy, space redundancy, structural redundancy, knowledge redundancy, visual redundancy, etc. Data compression is essentially to reduce these redundancies. Less redundancy in time can reduce the amount of data of the entire source without reducing the amount of information in the source.
[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A WebP image compression system, characterized in that: The system includes an image compression coding system, which includes image coding and image decoding; The image compression system compresses as follows: Pixel coding: Each pixel is processed individually during coding, such as pulse code modulation, entropy coding and run length coding; Predictive coding: By removing the correlation and redundancy between adjacent pixels, only new information is encoded. Commonly used methods include differential pulse code modulation; Transform coding: applying a certain transformation to a given image so that a large amount of information can be represented by less data; commonly used transforms include: discrete Fourier transform, discrete residual transform and discrete wavelet transform.
2. A Webp image compression system according to claim 1, characterized in that: There are three types of data redundancy: coding redundancy, inter-pixel redundancy and psychological visual redundancy. When one or more of them are reduced or eliminated, image compression is achieved.
3. A Webp image compression system according to claim 1, characterized in that: The system includes an information transmission system, which includes an encoder, a decoder and signal transmission.
4. A Webp image compression system according to claim 1, characterized in that: The system includes a codec model, which includes a transformer and a quantizer. The transformer converts the input data, changes the description form of the data, and reduces or eliminates redundancy between pixels; the quantizer reduces the accuracy of the transformer output according to a given fidelity criterion to further reduce psychological visual redundancy.
5. A Webp image compression system according to claim 1, characterized in that: The fidelity criterion is used during the image encoding to evaluate the degree of distortion or quality of the image so as to limit the image distortion within a given range.
6. A Webp image compression system according to claim 1, characterized in that: Image coding: compressing and encoding images before storage, processing and transmission, also known as image compression; Image decoding: Decompressing a compressed image to reconstruct the original image or its approximation.
7. The compression method of a Webp image compression system according to claim 1, characterized in that: The user can select one of the compression methods to compress the image according to personal needs.